# A Safe Screening Rule for Sparse Logistic Regression

arXiv: https://arxiv.org/abs/1307.4145 · HTML: https://mlsanity.com/paper/1307.4145/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2013.

## Built on by (most cited first, 14 of 14)

- [Safe Screening for Logistic Regression with ℓ0-ℓ2 Regularization](https://mlsanity.com/paper/2202.00467/) (2022)
- [The Challenge of Differentially Private Screening Rules](https://mlsanity.com/paper/2303.10303/) (2023)
- [The Hessian Screening Rule](https://mlsanity.com/paper/2104.13026/) (2021)
- [Generalized Low-Rank Update: Model Parameter Bounds for Low-Rank Training Data Modifications](https://mlsanity.com/paper/2306.12670/) (2023)
- [Screening for Sparse Online Learning](https://mlsanity.com/paper/2101.06982/) (2021)
- [One to beat them all: "RYU" -- a unifying framework for the construction of safe balls](https://mlsanity.com/paper/2312.00640/) (2023)
- [Strong Screening Rules for Group-based SLOPE Models](https://mlsanity.com/paper/2405.15357/) (2024)
- [Differentially Private Iterative Screening Rules for Linear Regression](https://mlsanity.com/paper/2502.18578/) (2025)
- [Safe Distributionally Robust Feature Selection under Covariate Shift](https://mlsanity.com/paper/2603.16062/) (2026)
- [Safe Screening for Sparse Conditional Random Fields](https://mlsanity.com/paper/2111.13958/) (2021)
- [A Safe Screening Rule with Bi-level Optimization of nu Support Vector Machine](https://mlsanity.com/paper/2403.01769/) (2024)
- [Dual Feature Reduction for the Sparse-group Lasso and its Adaptive Variant](https://mlsanity.com/paper/2405.17094/) (2024)
- [Safe Screening Rules for Group SLOPE](https://mlsanity.com/paper/2506.09451/) (2025)
- [Simpler Methods Work Better for L1 Penalized Logistic Models and Large Datasets](https://mlsanity.com/paper/2609.23995/) (2026)

## Abstract (arXiv)

The l1-regularized logistic regression (or sparse logistic regression) is a widely used method for simultaneous classification and feature selection. Although many recent efforts have been devoted to its efficient implementation, its application to high dimensional data still poses significant challenges. In this paper, we present a fast and effective sparse logistic regression screening rule (Slores) to identify the 0 components in the solution vector, which may lead to a substantial reduction in the number of features to be entered to the optimization. An appealing feature of Slores is that the data set needs to be scanned only once to run the screening and its computational cost is negligible compared to that of solving the sparse logistic regression problem. Moreover, Slores is independent of solvers for sparse logistic regression, thus Slores can be integrated with any existing solver to improve the efficiency. We have evaluated Slores using high-dimensional data sets from different applications. Extensive experimental results demonstrate that Slores outperforms the existing state-of-the-art screening rules and the efficiency of solving sparse logistic regression is improved by one magnitude in general.